{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 等高线图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "plt.rcParams['axes.unicode_minus'] = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3],\n",
       "       [1, 2, 3]])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([[4, 4, 4],\n",
       "       [5, 5, 5]])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "a = np.array([1,2,3])\n",
    "b = np.array([4,5])\n",
    "\n",
    "A, B = np.meshgrid(a,b)\n",
    "display(A, B)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(-5, 5, 100)\n",
    "y = np.linspace(-5, 5, 100)\n",
    "\n",
    "# 将 x 和 y 变成网格形式\n",
    "X, Y = np.meshgrid(x, y)\n",
    "\n",
    "# Z\n",
    "Z = np.sqrt(X**2 + Y**2)\n",
    "\n",
    "# 画等高线\n",
    "cb = plt.contourf(X, Y, Z)\n",
    "# 颜色调\n",
    "plt.colorbar(cb)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
